SoroushVahidi/scidocs huggingface dataset:
Fast Optimal Transport Averaging of Neuroimaging Data
(2015)

Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before performing a linear group averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of the entropic smoothing approach used by. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.
Deep Learning with Edge Computing for Localization of Epileptogenicity Using Multimodal rs-fMRI and EEG Big Data
(2017)

Epilepsy is a chronic brain disorder characterized by the occurrence of spontaneous seizures of which about 30 percent of patients remain medically intractable and may undergo surgical intervention; despite the latter, some may still fail to attain a seizure-free outcome. Functional changes may precede structural ones in the epileptic brain and may be detectable using existing noninvasive modalities. Functional connectivity analysis through electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI), complemented by diffusion tensor imaging (DTI), has provided such meaningful input in cases of temporal lobe epilepsy (TLE). Recently, the emergence of edge computing has provided competent solutions enabling context-aware and real-time response services for users. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for the monitoring, evaluation and regulation of the epileptic brain, with responsive neurostimulation (RNS; Neuropace). First, an autonomic edge computing framework is proposed for processing of big data as part of a decision support system for surgical candidacy. Second, an optimized model for estimation of the epileptogenic network using independently acquired EEG and rs-fMRI is presented. Third, an unsupervised feature extraction model is developed based on a convolutional deep learning structure for distinguishing interictal epileptic discharge (IED) periods from nonIED periods using electrographic signals from electrocorticography (ECoG). Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods.
The sounds of silence: language, cognition, and anxiety in selective mutism.
(2007)

OBJECTIVES
To determine whether oral language, working memory, and social anxiety differentiate children with selective mutism (SM), children with anxiety disorders (ANX), and normal controls (NCs) and explore predictors of mutism severity.


METHOD
Children ages 6 to 10 years with SM (n = 44) were compared with children with ANX (n = 28) and NCs (n = 19) of similar age on standardized measures of language, nonverbal working memory, and social anxiety. Variables correlating with mutism severity were entered in stepwise regressions to determine predictors of mute behavior in SM.


RESULTS
Children with SM scored significantly lower on standardized language measures than children with ANX and NCs and showed greater visual memory deficits and social anxiety relative to these two groups. Age and receptive grammar ability predicted less severe mutism, whereas social anxiety predicted more severe mutism. These factors accounted for 38% of the variance in mutism severity.


CONCLUSIONS
Social anxiety and language deficits are evident in SM, may predict mutism severity, and should be evaluated in clinical assessment. Replication is indicated, as are further studies of cognition and of intervention in SM, using large, diverse samples.
Collaborative Filtering Service Recommendation Based on a Novel Similarity Computation Method
(2017)

Recently, collaborative filtering-based methods are widely used for service recommendation. QoS attribute value-based collaborative filtering service recommendation mainly includes two important steps. One is the similarity computation, and the other is the prediction for QoS attribute value, which the user has not experienced. In previous studies, the performances of some methods need to be improved. In this paper, we propose a ratio-based method to calculate the similarity. We can get the similarity between users or between items by comparing the attribute values directly. Based on our similarity computation method, we propose a new method to predict the unknown value. By comparing the values of a similar service and the current service that are invoked by common users, we can obtain the final prediction result. The performance of the proposed method is evaluated through a large data set of real web services. Experimental results show that our method obtains better prediction precision, lower mean absolute error ( <inline-formula><tex-math notation="LaTeX">$MAE$</tex-math><alternatives> <inline-graphic xlink:href="wu-ieq1-2479228.gif"/></alternatives></inline-formula>) and faster computation time than various reference schemes considered.
Relative Physical Knowledge of Actions and Objects
(2017)

Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.
Smart cities based on web semantic technologies
(2016)

A good communication and interaction between citizens and the administration is important and crucial, also can greatly help in improving the quality of urban life of citizen. In this paper, we propose a semantic data model for managing and resolving the problems that exist in cities such as water leak, street faults, broken street lights, and potholes. The main idea is to focus on the best practices of linked open data to describe all issues, and then integrate them in the dataset provided by DBpedia. Hence, our approach is based on the standards of the World Wide Web Consortium.
A High-Voltage Pulse Generator With Continuously Variable Pulsewidth Based on a Modified PFN
(2017)

The amount of stored energy is an important issue in the low-droop and high-precision pulse generation. Pulse forming network (PFN) simulating an open-ended transmission line seems to be useful due to its minimum stored energy. The drawbacks of the PFN include low-output pulse quality and constant pulsewidth, which limit their applications. Another limitation of generating rectangular pulses using PFN is that half of the charged voltage appears in the output when delivering the entire stored energy to the matched load. This paper proposes a structure of PFN-based pulse generator that overcomes the above-mentioned limitations. For continuous pulsewidth adjustment, a lossless scheme is presented. By increasing the ratio of the load magnitude to the characteristic impedance, the amplitude of the output pulse voltage would be approximately equal to that of the charged voltage. Therefore, the semiconductor switches can be utilized in the PFN pulse generator easily. In addition, the pulse quality is enhanced and faster rising time is obtained in comparison with the conventional PFNs. In this proposal, the stored energy quantity is limited to three times the energy needed for the output pulse in the case of  $R_{L}=10Z_{0}$ . To verify the proposed structure, a prototype is simulated using PSPICE software, and the experimental results are presented.
Is it a tool suitable for learning? A critical review of the literature on Facebook as a technology-enhanced learning environment
(2013)

Despite its continuing popularity as the social network site par excellence, the educational value of Facebook has not been fully determined, and results from the mainstream educational paradigms are contradictory, with some scholars emphasizing its pedagogical affordances (e.g., widening context of learning, mixing information and learning resources, hybridization of expertise) and others cautioning against its use for educational purposes. Moreover, systematic reviews about documented educational usage of Facebook as a learning environment are lacking. This article attempts to provide a critical overview of current studies focusing on the use of Facebook as a technology-enhanced learning environment, with the aim of exploring the extent to which its pedagogical potential is actually translated into practice. Only empirical studies published in peer-reviewed academic journals with a specific focus on Facebook as a learning environment have been considered for the review. The authors conducted a comprehensive literature search that identified 23 relevant articles that were subsequently analysed according to a simplified list of guidelines. These articles were further analysed and recoded through a set of emerging categories. The results show that pedagogical affordances of Facebook have only been partially implemented and that there are still many obstacles that may prevent a full adoption of Facebook as a learning environment such as implicit institutional, teacher and student pedagogies, and cultural issues. Finally, a broad observation on the implications of the study is developed with some suggestions for future research.
A Next-Generation Secure Cloud-Based Deep Learning License Plate Recognition for Smart Cities
(2016)

License Plate Recognition System (LPRS) plays a vital role in smart city initiatives such as traffic control, smart parking, toll management and security. In this article, a cloud-based LPRS is addressed in the context of efficiency where accuracy and speed of processing plays a critical role towards its success. Signature-based features technique as a deep convolutional neural network in a cloud platform is proposed for plate localization, character detection and segmentation. Extracting significant features makes the LPRS to adequately recognize the license plate in a challenging situation such as i) congested traffic with multiple plates in the image ii) plate orientation towards brightness, iii) extra information on the plate, iv) distortion due to wear and tear and v) distortion about captured images in bad weather like as hazy images. Furthermore, the deep learning algorithm computed using bare-metal cloud servers with kernels optimized for NVIDIA GPUs, which speed up the training phase of the CNN LPDS algorithm. The experiments and results show the superiority of the performance in both recall and precision and accuracy in comparison with traditional LP detecting systems.
Pornographic image detection utilizing deep convolutional neural networks
(2016)

Many internet users are potential victims of the pornographic images and a large part of them are underage children. Thus, content-based pornographic images detection is an important task in computer vision and multimedia research. Previous solutions usually rely on hand-engineered visual features that are hard to analyze and select. In this paper, to detect pornographic images in any style accurately and efficiently with a single model, a novel scheme utilizing the deep convolutional neural networks (CNN) is proposed. The training data are obtained from internet followed by an improved sliding window method and some novel data augmentation approaches. Then a highly efficient training algorithm is proposed based on two strategies. The first is the pre-trained midlevel representations non-fixed fine-tuning strategy. The second is adjusting the training data at the appropriate time on the basis of the performance of the proposed network on the validation set. Furthermore, we introduce a fast image scanning method which is also based on the sliding window approach in the test. We further propose a fast forward pass method based on the “fixedpoint algorithm”. So our CNN could detect all scale images so fast by one forward pass. The effectiveness of the proposed method is demonstrated in experiments on the proposed dataset and the comparative results show that our ∗Corresponding author Email address: tenglwy@gmail.com (Teng Li) Preprint submitted to Neurocomputing June 9, 2016 method lead to state-of-the-art detection performance.
A Novel DC-Side Zero-Voltage Switching (ZVS) Three-Phase Boost PWM Rectifier Controlled by an Improved SVM Method
(2012)

A novel active clamping zero-voltage switching three-phase boost pulsewidth modulation (PWM) rectifier is analyzed and a modified minimum-loss space vector modulation (SVM) strategy suitable for the novel zero-voltage switching (ZVS) rectifier is proposed in this paper. The topology of the novel ZVS rectifier only adds one auxiliary active switch, one resonant inductor, and one clamping capacitor to the traditional hard-switched three-phase boost PWM rectifier. With the proposed SVM strategy, the novel ZVS rectifier can achieve ZVS for all the main and auxiliary switches. In addition, the antiparallel diodes can be turned OFF softly, so the reverse recovery current is eliminated. Besides, the voltage stress of all the switches is equal to the dc-link voltage. The operation principle and soft-switching condition of the novel ZVS rectifier are analyzed. The design guidelines of the soft switched circuit parameters are described in detail. A DSP controlled 30 kW prototype is implemented to verify the theory.
Using heuristics to estimate an appropriate number of latent topics in source code analysis
(2013)

Latent Dirichlet Allocation (LDA) is a data clustering algorithm that performs especially well for text documents. In natural-language applications it automatically finds groups of related words (called “latent topics”) and clusters the documents into sets that are about the same “topic”. LDA has also been applied to source code, where the documents are natural source code units such as methods or classes, and the words are the keywords, operators, and programmer-defined names in the code. The problem of determining a topic count that most appropriately describes a set of source code documents is an open problem. We address this empirically by constructing clusterings with different numbers of topics for a large number of software systems, and then use a pair of measures based on source code locality and topic model similarity to assess how well the topic structure identifies related source code units. Results suggest that the topic count required can be closely approximated using the number of software code fragments in the system. We extend these results to recommend appropriate topic counts for arbitrary software systems based on an analysis of a set of open source systems.
Standard-Compliant Multiview Video Coding and Streaming for Virtual Reality Applications
(2016)

Virtual reality (VR) systems employ multiview cameras or camera rigs to capture a scene from the entire 360-degree perspective. Due to computational or latency constraints, it might not be possible to stitch multiview videos into a single video sequence prior to encoding. In this paper we investigate the coding and streaming of multiview VR video content. We present a standard-compliant method where we first divide the camera views into two types: Primary views represent a subset of camera views with lower resolution and non-overlapping (minimally overlapping) content which cover the entire 360-degree field-of-view to guarantee immediate monoscopic viewing during very rapid head movements. Auxiliary views consist of remaining camera views with higher resolution which produce overlapping content with the primary views and are additionally used for stereoscopic viewing. Based on this categorization, we propose a coding arrangement in which, the primary views are independently coded in the base layer and the additional auxiliary views are coded as an enhancement layer, using inter-layer prediction from primary views. The proposed system not only meets the low latency requirements of VR systems, but also conforms to the existing multilayer extensions of the High Efficiency Video Coding standard. Simulation results show that the coding and streaming performance of the proposed scheme is significantly improved compared to earlier methods.
Audio-based context recognition
(2006)

The aim of this paper is to investigate the feasibility of an audio-based context recognition system. Here, context recognition refers to the automatic classification of the context or an environment around a device. A system is developed and compared to the accuracy of human listeners in the same task. Particular emphasis is placed on the computational complexity of the methods, since the application is of particular interest in resource-constrained portable devices. Simplistic low-dimensional feature vectors are evaluated against more standard spectral features. Using discriminative training, competitive recognition accuracies are achieved with very low-order hidden Markov models (1-3 Gaussian components). Slight improvement in recognition accuracy is observed when linear data-driven feature transformations are applied to mel-cepstral features. The recognition rate of the system as a function of the test sequence length appears to converge only after about 30 to 60 s. Some degree of accuracy can be achieved even with less than 1-s test sequence lengths. The average reaction time of the human listeners was 14 s, i.e., somewhat smaller, but of the same order as that of the system. The average recognition accuracy of the system was 58% against 69%, obtained in the listening tests in recognizing between 24 everyday contexts. The accuracies in recognizing six high-level classes were 82% for the system and 88% for the subjects.
An overview of audio information retrieval
(1999)

The problem of audio information retrieval is familiar to anyone who has returned from vacation to find an answering machine full of messages. While there is not yet an “AltaVista” for the audio data type, many workers are finding ways to automatically locate, index, and browse audio using recent advances in speech recognition and machine listening. This paper reviews the state of the art in audio information retrieval, and presents recent advances in automatic speech recognition, word spotting, speaker and music identification, and audio similarity with a view towards making audio less “opaque”. A special section addresses intelligent interfaces for navigating and browsing audio and multimedia documents, using automatically derived information to go beyond the tape recorder metaphor.
Fast and robust fixed-point algorithms for independent component analysis
(1999)

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.
Programming language evolution
(2017)

Programming languages are the way developers communicate with computers—just like natural languages let us communicate with one another. In both cases multiple languages have evolved. However, the programming language ecosystem changes at a much higher rate compared to natural languages. In fact, programming languages need to constantly evolve in response to user needs, hardware advances and research developments or they are otherwise displaced by newcomers. As a result, existing code written by developers may stop working with a newer language version. Consequently, developers need to search (analyse) and replace (refactor) code in their code bases to support new language versions. Traditionally, tools have focused on the replace aspect (refactoring) to support developers evolving their code bases. This dissertation argues that developers also need machine support focused on the search aspect. This dissertation starts by characterising factors driving programming language evolution based on external versus internal forces. Next, it introduces a classification of changes introduced by language designers that affect developers. It then contributes three techniques to support developers in analysing their code bases. First, we show a source code query system based on graphs that express code queries at a mixture of syntax-tree, type, control-flow graph and data-flow levels. We demonstrate how this system can support developers and language designers in locating various code patterns relevant in evolution. Second, we design an optional run-time type system for Python, that lets developers manually specify contracts to identify semantic incompatibilities between Python 2 and Python 3. Third, recognising that existing codebases do not have such contracts, we describe a dynamic analysis to automatically generate them.
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
(2019)

User interaction with voice-powered agents generates large amounts of unlabeled utterances. In this paper, we explore techniques to efficiently transfer the knowledge from these unlabeled utterances to improve model performance on Spoken Language Understanding (SLU) tasks. We use Embeddings from Language Model (ELMo) to take advantage of unlabeled data by learning contextualized word representations. Additionally, we propose ELMo-Light (ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer. Additionally, we show that the gains from unsupervised transfer techniques can be further improved by supervised transfer. The improvements are more pronounced in low resource settings and when using only 1000 labeled in-domain samples, our techniques match the performance of training from scratch on 10-15x more labeled in-domain data.